# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest
from functools import partial
from typing import Any, Dict, List

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertPreluTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input(batch, dim1, dim2, dim3):
            shape = [batch]
            if dim1 != 0:
                shape.append(dim1)
            if dim2 != 0:
                shape.append(dim2)
            if dim3 != 0:
                shape.append(dim3)
            return np.random.random(shape).astype(np.float32)

        def generate_alpha(attrs: List[Dict[str, Any]], dim1, dim2, dim3):
            if attrs[0]["mode"] == "all":
                return np.random.random(size=(1)).astype(np.float32)
            elif (
                attrs[0]["mode"] == "channel"
                and attrs[0]["data_format"] == "NCHW"
            ):
                shape = [1]
                if dim1 != 0:
                    shape.append(dim1)
                if dim2 != 0:
                    shape.append(dim2)
                if dim3 != 0:
                    shape.append(dim3)
                return np.random.random(size=shape[1]).astype(np.float32)
            elif (
                attrs[0]["mode"] == "channel"
                and attrs[0]["data_format"] == "NHWC"
            ):
                shape = [1]
                if dim1 != 0:
                    shape.append(dim1)
                if dim2 != 0:
                    shape.append(dim2)
                if dim3 != 0:
                    shape.append(dim3)
                return np.random.random(size=shape[-1]).astype(np.float32)
            elif attrs[0]["mode"] == "element":
                shape = [1]
                if dim1 != 0:
                    shape.append(dim1)
                if dim2 != 0:
                    shape.append(dim2)
                if dim3 != 0:
                    shape.append(dim3)
                return np.random.random(size=shape).astype(np.float32)

        for batch in [1, 4]:
            for dim1 in [0, 3]:
                for dim2 in [0, 16]:
                    for dim3 in [0, 32]:
                        self.dim1 = dim1
                        self.dim2 = dim2
                        self.dim3 = dim3

                        if dim1 == 0 and dim2 != 0:
                            continue
                        if dim1 == 0 and dim2 == 0 and dim3 != 0:
                            continue

                        for mode in ["all", "channel", "element"]:
                            for data_format in ['NCHW', 'NHWC']:
                                if (
                                    mode == "channel"
                                    and dim1 == 0
                                    and data_format == "NCHW"
                                ):
                                    continue
                                if (
                                    mode == "channel"
                                    and dim3 == 0
                                    and data_format == "NHWC"
                                ):
                                    continue
                                dics = [
                                    {"mode": mode, "data_format": data_format}
                                ]
                                ops_config = [
                                    {
                                        "op_type": "prelu",
                                        "op_inputs": {
                                            "X": ["input_data"],
                                            "Alpha": ["alpha_weight"],
                                        },
                                        "op_outputs": {"Out": ["output_data"]},
                                        "op_attrs": dics[0],
                                    }
                                ]
                                ops = self.generate_op_config(ops_config)

                                program_config = ProgramConfig(
                                    ops=ops,
                                    weights={
                                        "alpha_weight": TensorConfig(
                                            data_gen=partial(
                                                generate_alpha,
                                                dics,
                                                dim1,
                                                dim2,
                                                dim3,
                                            )
                                        )
                                    },
                                    inputs={
                                        "input_data": TensorConfig(
                                            data_gen=partial(
                                                generate_input,
                                                batch,
                                                dim1,
                                                dim2,
                                                dim3,
                                            )
                                        ),
                                    },
                                    outputs=["output_data"],
                                )

                                yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> (paddle_infer.Config, List[int], float):
        def generate_dynamic_shape(attrs):
            if self.dim1 == 0:
                self.dynamic_shape.min_input_shape = {
                    "input_data": [1],
                }
                self.dynamic_shape.max_input_shape = {
                    "input_data": [4],
                }
                self.dynamic_shape.opt_input_shape = {
                    "input_data": [2],
                }
            else:
                if self.dim2 == 0 and self.dim3 == 0:
                    self.dynamic_shape.min_input_shape = {
                        "input_data": [1, 1],
                    }
                    self.dynamic_shape.max_input_shape = {
                        "input_data": [4, 32],
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "input_data": [2, 3],
                    }
                elif self.dim2 != 0 and self.dim3 != 0:
                    self.dynamic_shape.min_input_shape = {
                        "input_data": [1, 1, 1, 1],
                    }
                    self.dynamic_shape.max_input_shape = {
                        "input_data": [4, 3, 16, 32],
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "input_data": [2, 3, 16, 32],
                    }
                elif self.dim3 == 0:
                    self.dynamic_shape.min_input_shape = {
                        "input_data": [1, 1, 1],
                    }
                    self.dynamic_shape.max_input_shape = {
                        "input_data": [4, 3, 32],
                    }
                    self.dynamic_shape.opt_input_shape = {
                        "input_data": [2, 3, 16],
                    }

        def clear_dynamic_shape():
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if (
                not dynamic_shape
                and self.dim1 == 0
                and self.dim2 == 0
                and self.dim3 == 0
            ):
                return 0, 3
            return 1, 2

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False
        ), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False
        ), (1e-3, 1e-3)

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True
        ), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True
        ), (1e-3, 1e-3)

    def add_skip_trt_case(self):
        ver = paddle_infer.get_trt_compile_version()
        if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000:

            def teller(program_config, predictor_config):
                if not predictor_config.tensorrt_dynamic_shape_enabled():
                    return True
                return False

            self.add_skip_case(
                teller,
                SkipReasons.TRT_NOT_IMPLEMENTED,
                "Need to repair the case: the output of GPU and tensorrt has diff in trt6, the prelu static plugin has bug.",
            )

    def test(self):
        self.add_skip_trt_case()
        self.run_test()


if __name__ == "__main__":
    unittest.main()
